{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from linearmodels.panel import compare\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from linearmodels.panel import PanelOLS, RandomEffects\n",
    "from scipy.stats import chi2\n",
    "from statsmodels.stats.diagnostic import het_breuschpagan\n",
    "from statsmodels.stats.stattools import durbin_watson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "file_path = '../data/CA2_filled.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Data preprocessing: dependent and independent variables\n",
    "# Dependent variable: log(GDP)\n",
    "# Independent variables: edu_m, edu_f, gra_m, gra_f, gee, er_m, er_f, law, cpi\n",
    "\n",
    "# Convert variables to appropriate data types (ensure correctness)\n",
    "data['Country'] = data['Country'].astype('category')\n",
    "data['Year'] = data['Year'].astype('int')\n",
    "\n",
    "# Apply log transformation to gdp\n",
    "data['log_gdp'] = np.log(data['gdp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gpi'] = data['edu_f'] / data['edu_m']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multicollinearity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variance Inflation Factor (VIF) before removing variables:\n",
      "    Variable         VIF\n",
      "0    log_gdp    2.741991\n",
      "1        gee    2.558792\n",
      "2         er    1.087863\n",
      "3        law    1.984679\n",
      "4        cpi    1.563001\n",
      "5  Intercept  235.433058\n"
     ]
    }
   ],
   "source": [
    "# Multicollinearity check using Variance Inflation Factor (VIF)\n",
    "print(\"\\nVariance Inflation Factor (VIF) before removing variables:\")\n",
    "X = data[['log_gdp', 'gee', 'er', 'law', 'cpi']].copy()\n",
    "X.loc[:, 'Intercept'] = 1  # Add intercept for VIF calculation\n",
    "vif_data_before = pd.DataFrame()\n",
    "vif_data_before['Variable'] = X.columns\n",
    "vif_data_before['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(vif_data_before)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hausman test---choose RE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hausman Test:\n",
      "Hausman Statistic: 4.563291286703915\n",
      "P-value: 0.47146020002363476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Hausman test for fixed vs. random effects\n",
    "# Using linearmodels library for panel data regression\n",
    "from linearmodels.panel import compare\n",
    "\n",
    "# Fixed effects model\n",
    "fixed_effects = PanelOLS.from_formula('gpi ~ log_gdp + gee + er + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Random effects model\n",
    "random_effects = RandomEffects.from_formula('gpi ~ log_gdp + gee + er + law + cpi', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Standard Hausman test\n",
    "print(\"\\nHausman Test:\")\n",
    "fe_params = fixed_effects.params\n",
    "re_params = random_effects.params\n",
    "common_params = fe_params.index.intersection(re_params.index)\n",
    "\n",
    "b_diff = fe_params[common_params] - re_params[common_params]\n",
    "V_fe = fixed_effects.cov.loc[common_params, common_params]\n",
    "V_re = random_effects.cov.loc[common_params, common_params]\n",
    "V_diff = V_fe - V_re\n",
    "\n",
    "hausman_stat = b_diff.T @ np.linalg.inv(V_diff) @ b_diff\n",
    "p_value = 1 - chi2.cdf(hausman_stat, len(common_params))\n",
    "\n",
    "print(f\"Hausman Statistic: {hausman_stat}\")\n",
    "print(f\"P-value: {p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Gender Parity Index over time for each country using seaborn\n",
    "plt.figure(figsize=(12, 6))\n",
    "for country in data['Country'].unique():\n",
    "    country_data = data[data['Country'] == country]\n",
    "    plt.plot(country_data['Year'], country_data['gpi'], label=country)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GPI')\n",
    "plt.title('GPI over Time by Country')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# without interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Effects Regression Results:\n",
      "                        RandomEffects Estimation Summary                        \n",
      "================================================================================\n",
      "Dep. Variable:                    gpi   R-squared:                        0.6559\n",
      "Estimator:              RandomEffects   R-squared (Between):              0.9752\n",
      "No. Observations:                 220   R-squared (Within):               0.1078\n",
      "Date:                Wed, Oct 23 2024   R-squared (Overall):              0.9658\n",
      "Time:                        23:10:18   Log-likelihood                    201.05\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      81.977\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(5,215)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             121.22\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(5,215)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        0.1294     0.0214     6.0383     0.0000      0.0872      0.1717\n",
      "gee            0.0140     0.0122     1.1461     0.2530     -0.0101      0.0380\n",
      "er            -0.0058     0.0034    -1.7275     0.0855     -0.0125      0.0008\n",
      "law           -0.0805     0.0597    -1.3480     0.1791     -0.1982      0.0372\n",
      "cpi            0.0035     0.0030     1.1532     0.2501     -0.0025      0.0094\n",
      "==============================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Random effects regression\n",
    "re_model = RandomEffects.from_formula('gpi ~ log_gdp + gee + er + law + cpi', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result\n",
    "print(\"\\nRandom Effects Regression Results:\")\n",
    "print(re_model.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# with interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create interaction terms\n",
    "data['law_er'] = data['law'] * data['er']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Effects Regression Results:\n",
      "                        RandomEffects Estimation Summary                        \n",
      "================================================================================\n",
      "Dep. Variable:                    gpi   R-squared:                        0.8017\n",
      "Estimator:              RandomEffects   R-squared (Between):              0.9873\n",
      "No. Observations:                 220   R-squared (Within):               0.3364\n",
      "Date:                Wed, Oct 23 2024   R-squared (Overall):              0.9802\n",
      "Time:                        23:10:18   Log-likelihood                    233.59\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      144.16\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,214)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             279.10\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,214)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        0.0860     0.0158     5.4606     0.0000      0.0550      0.1171\n",
      "gee           -0.0080     0.0093    -0.8634     0.3889     -0.0262      0.0103\n",
      "er             0.0022     0.0026     0.8644     0.3884     -0.0029      0.0073\n",
      "law            2.0325     0.3126     6.5013     0.0000      1.4163      2.6488\n",
      "cpi           -0.0033     0.0029    -1.1320     0.2589     -0.0089      0.0024\n",
      "law_er        -0.0319     0.0050    -6.3996     0.0000     -0.0417     -0.0221\n",
      "==============================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Random effects regression\n",
    "re_model = RandomEffects.from_formula('gpi ~ log_gdp + gee + er + law + cpi + law_er', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result\n",
    "print(\"\\nRandom Effects Regression Results:\")\n",
    "print(re_model.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robustness check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Effects Regression Results with Interaction Terms and kernel Standard Errors:\n",
      "                        RandomEffects Estimation Summary                        \n",
      "================================================================================\n",
      "Dep. Variable:                    gpi   R-squared:                        0.8017\n",
      "Estimator:              RandomEffects   R-squared (Between):              0.9873\n",
      "No. Observations:                 220   R-squared (Within):               0.3364\n",
      "Date:                Wed, Oct 23 2024   R-squared (Overall):              0.9802\n",
      "Time:                        23:10:19   Log-likelihood                    233.59\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      144.16\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,214)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             197.03\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,214)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        0.0860     0.0231     3.7286     0.0002      0.0406      0.1315\n",
      "gee           -0.0080     0.0162    -0.4945     0.6215     -0.0399      0.0239\n",
      "er             0.0022     0.0026     0.8605     0.3905     -0.0029      0.0074\n",
      "law            2.0325     0.4268     4.7627     0.0000      1.1913      2.8737\n",
      "cpi           -0.0033     0.0033    -0.9973     0.3197     -0.0097      0.0032\n",
      "law_er        -0.0319     0.0068    -4.6660     0.0000     -0.0454     -0.0184\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "# Random effects regression with interaction term and kernel standard errors\n",
    "re_model = RandomEffects.from_formula('gpi ~ log_gdp + gee + er + law + cpi + law_er', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result\n",
    "print(\"\\nRandom Effects Regression Results with Interaction Terms and kernel Standard Errors:\")\n",
    "print(re_model.summary)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
